Algerian Forest Fires
Donated on 10/21/2019
The dataset includes 244 instances that regroup a data of two regions of Algeria.
Dataset Characteristics
Multivariate
Subject Area
Biology
Associated Tasks
Classification, Regression
Feature Type
Real
# Instances
244
# Features
14
Dataset Information
Additional Information
The dataset includes 244 instances that regroup a data of two regions of Algeria,namely the Bejaia region located in the northeast of Algeria and the Sidi Bel-abbes region located in the northwest of Algeria. 122 instances for each region. The period from June 2012 to September 2012. The dataset includes 11 attribues and 1 output attribue (class) The 244 instances have been classified into ‘fire’ (138 classes) and ‘not fire’ (106 classes) classes.
Has Missing Values?
No
Introductory Paper
By Faroudja Abid, N.Izeboudjen. 2020
Published in Ezziyyani M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). Advances in Intelligent Systems and Computing
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| region | Feature | Categorical | Bejaia or Sidi-Bel Abbes | no | |
| day | Feature | Integer | no | ||
| month | Feature | Integer | no | ||
| year | Feature | Integer | no | ||
| Temperature | Feature | Integer | temperature noon | C | no |
| RH | Feature | Integer | relative humidity | % | no |
| Ws | Feature | Integer | wind speed | km/h | no |
| Rain | Feature | Continuous | mm | no | |
| FFMC | Feature | Continuous | Fine Fuel Moisture Code | no | |
| DMC | Feature | Continuous | Duff Moisture Code | no |
0 to 10 of 15
Additional Variable Information
1. Date : (DD/MM/YYYY) Day, month ('june' to 'september'), year (2012) Weather data observations 2. Temp : temperature noon (temperature max) in Celsius degrees: 22 to 42 3. RH : Relative Humidity in %: 21 to 90 4. Ws :Wind speed in km/h: 6 to 29 5. Rain: total day in mm: 0 to 16.8 FWI Components 6. Fine Fuel Moisture Code (FFMC) index from the FWI system: 28.6 to 92.5 7. Duff Moisture Code (DMC) index from the FWI system: 1.1 to 65.9 8. Drought Code (DC) index from the FWI system: 7 to 220.4 9. Initial Spread Index (ISI) index from the FWI system: 0 to 18.5 10. Buildup Index (BUI) index from the FWI system: 1.1 to 68 11. Fire Weather Index (FWI) Index: 0 to 31.1 12. Classes: two classes, namely “Fire†and “not Fireâ€
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset algerian_forest_fires = fetch_ucirepo(id=547) # data (as pandas dataframes) X = algerian_forest_fires.data.features y = algerian_forest_fires.data.targets # metadata print(algerian_forest_fires.metadata) # variable information print(algerian_forest_fires.variables)
Abid, Faroudja. (2019). Algerian Forest Fires. UCI Machine Learning Repository. https://doi.org/10.24432/C5KW4N.
@misc{misc_algerian_forest_fires_547,
author = {Abid, Faroudja},
title = {{Algerian Forest Fires}},
year = {2019},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5KW4N}
}Creators
Faroudja Abid
fabid@cdta.dz
Center for Development of Advanced Technologies (CDTA)
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.